Services
AI-native
engineering.
We build with agentic pipelines by default. Faster delivery, tighter feedback loops, fewer manual quality gates.
What this means in practice
Before we bought the $20 Claude subscription, we were running multi-agent workflows across Gemini Pro tabs. Copy-pasting outputs between sessions. Keeping state in our heads. Manually picking up context and feeding it into the next window. It worked until the overhead ate the work.
The Claude subscription changed what felt possible. Not just the model. The architecture around it. Skills you could write once and reuse. Workflows that held context. That forced us to build the pipeline we now use for all client work.
The pipeline
Idea arrives
/idea-to-plan: grooming session, PRD, GitHub slice issues. The plan exists before a single line of code.
Executor agent
Reads the codebase, plans the phase, writes the code. Commits to the feature branch. Never pushes without review.
Reviewer agent
Fresh context. Reads only the diff. Derives its verdict independently. No investment in the chosen approach.
Fixer agent (if needed)
Addresses reviewer findings. A second reviewer pass. Two loops maximum before the pipeline pings a human.
Ship
Push, PR, CI gate, auto-merge. The pipeline knows when it's stuck and stops trying instead of guessing.
Learnings captured
What worked, what didn't, what to do differently. Committed with the phase. Not lost when the session ends.
What we build
Agentic development pipelines
We build and run the same agentic pipeline for client work that we run for our own products. Issue-to-phase-to-branch, adversarial review and fix loops, auto-merge on approval. Faster delivery, fewer manual gates.
- GSD-based project management
- Adversarial review automation
- Auto-merge and CI integration
- Skill and workflow design
Local RAG and knowledge base setup
Your information is scattered across Notion, Slack, PDFs, and markdown files. A local RAG stack indexes everything and makes it retrievable at inference time. We run LightRAG and BGE-M3 embeddings on a local RTX 3090. No cloud dependency, no per-query billing.
- LightRAG knowledge base setup
- BGE-M3 embedding pipeline
- Document ingestion workflows
- Query interface integration
Content and automation pipelines
Ideas to published content, without manual handoffs. Idea capture, adversarial review, blog draft generation in brand voice, social format adaptation, publish hook. All automated. All running on the same codebase we use internally.
- Idea capture and review workflow
- Blog draft generation in voice
- Social format adaptation
- Publish and distribution hooks
How engagements work
Discovery sprint
One week, fixed-fee
We define the scope together, map the integration points, and hand back a written spec for a model integration or agent build that you own outright — whether or not you continue with us.
Build engagement
Typically four to twelve weeks
Scoped directly from the discovery output. Billed as fixed-fee per milestone so the cost is known before each stage begins. No hourly billing, no scope creep surprises.
Ongoing partnership
Month-to-month retainer
For clients with shipped systems that need a steady development partner. Iteration, maintenance, and new pipeline features on a cadence that fits your operation.
Want the pipeline running for your team?
We build and run agentic workflows for teams outside our own. One conversation to describe the process you want to automate. Book a 30-minute call.
Book a CallQuestions we get asked
- What is AI-native engineering?
- AI-native engineering means building software with an agentic development pipeline as the default, not as an add-on. We use Claude Code as the primary developer interface, write skills and workflows that hold context across sessions, and use adversarial review and fix loops to ship with fewer manual quality gates. The result is faster delivery and tighter feedback loops than a traditional agency engagement.
- How does this differ from 'using AI to write code'?
- Most teams using AI to write code are using it as a smarter autocomplete. AI-native engineering means the agent reads the codebase itself, holds the architecture in context, proposes changes at the system level, and runs its own review and fix loops before handing off. The developer's role shifts from writing code to writing the plan, reviewing the diff, and owning the judgment calls the agent can't make.
- What is a local RAG stack and when do founders need one?
- A RAG stack is a retrieval-augmented generation system: a knowledge base your AI can search at inference time. Founders need one when the information that answers their daily questions is scattered across Notion pages, contract PDFs, chat logs, and markdown files. We run a local stack on an RTX 3090 using LightRAG and BGE-M3 embeddings. The 3090 found its real job indexing our internal knowledge base after a failed attempt to run a coding agent locally.
- Can you build an agentic workflow for my team?
- Yes. We build custom skills, orchestration pipelines, and agentic workflows for teams that want to run the same kind of pipeline we run internally. This includes content pipelines, code review automation, data extraction workflows, and internal knowledge base setups. Book a call and describe the manual process you want to automate.
- What does the agentic development pipeline look like in practice?
- An idea arrives. It goes through /idea-to-plan: a grooming session, a PRD, and a set of GitHub slice issues. Each issue becomes a GSD phase. The executor agent plans and codes the phase. The reviewer agent reads only the diff and derives its verdict independently. If the reviewer requests changes, a fixer agent closes them. If two loops fail to converge, the pipeline stops and pings a human. Most phases ship in one loop.
- What does the AI-native approach cost compared to traditional development?
- The marginal cost of iteration drops significantly. A traditional agency bills hourly for discovery, planning, code review, and QA — all of which the pipeline runs automatically. The upfront cost is building the pipeline and the skills that run it. Once built, the cost per feature falls. We pass that asymmetry on: our engagements move faster and cost less per feature than traditional agency timelines.
- Do you need to be technical to benefit from AI-native workflows?
- The judgment calls still require domain expertise, and the most valuable input is often a founder explaining exactly how the current process breaks. You don't need to write code. You need to be able to describe the problem precisely enough that the pipeline can solve it. That precision is a skill we help you develop.
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